Qualidade de vida do servidor da Universidade Federal do Rio Grande do Norte em condição crônica de saúde

The study aimed to analyze the influence of chronic health conditions (CHC) on quality of life (QOL) of UFRN servers assaulted by CHC. It is a descriptive and cross-sectional study with prospective data and quantitative approach, accomplished in the ambulatory clinic of the Department of Server Assistance (DSA) of the Pro-Rectory of Human Resources, during three months. The sample was composed by accessibility, totaling 215 people, being 153 active and 62 inactive servers, in chronic health condition. The data were collected through the application of the sociodemographic characterization, health, environmental and laboral form, the Medical Outcome Study 36-Item Short Form (SF-36). The study was evaluated by the HUOL Ethics Committee (CAAE no. 0046.0.294.000.10), obtaining assent. The results were analyzed in the SPSS 15.0 program through the descriptive and inferential statistics. It was identified servants predominantly male (59,1%), under 60 years old, married or in stable union, Catholics, brown color, living in the capital and residents in own home. Regarding labor issues, there was a predominance of active servers technical-administrative with intermediate and medium level positions and small proportion of docents. Among the CHC, the non-communicable diseases - NCDs (95.8%) had a higher frequency, followed by persistent mental disorders - PMDs (18.6%) and, finally, the continuous and structural physical deficiency - CSPD (16.9 %). The QOL of servers was considered good, with a mean score of 72.5 points in the total score, with the most affected domains: physical (59.1), general health (66.2), bodily pain (66.3) and functional aspects (72.0). The mental health dimension (76.5) had a better average than the physical dimension (68.0 points). It was found that the decrease in QOL scores is significant statistically related to higher number of CHC (ρ <0.001), with no statistical significance regarding the functional situation (p = 0.259). The administrative technicians of elementary, primary, secondary levels and docents had the worst QOL scores. After the correlation analysis of CHC with the domains and dimensions of the SF-36, there was statistically significant, negative and weak correlation of the domains: functional aspect (ρ = 0.002, r = -0.207), physical aspects (ρ = 0.007; r = -0.183), vitality (ρ = 0.002, r = -0.213), social function (ρ = 0.000, r = -0.313), emotional aspects (ρ = 0.000, r = -0.293), mental health (ρ = 0.000 , r = -0.238), physical health dimension (ρ = 0.002, r = -0.210) and mental health dimension (ρ = 0.000, r = -0.298). The presence of PMD isolated or together, contributed to a lower SF-36 scores, being the domains variation of mean significant, except for bodily pain, general health and physical aspects. By correlating the categories of CHC and QOL, there was a weak correlation (r ≤ -0.376) and significant (ρ ≤ 0.011), mainly related to the NCD, PMDs and NCD + PMD, affecting the mental health, social function, emotional aspects, vitality and functional aspect domains. Front of the results, it was concludes that the servers quality of life is influenced by the CHC. Thus, it was inferred that the presence of CHC causes a negative effect on quality of life, leading the active and inactive servers to exposure their overall life activities and work over the years, due to the morbidity affected, mainly related to NCDs and PMDs. Descriptors: Quality of life. Chronic disease. Occupational Health. Nursing

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Natural infection of gastrointestinal nematodes in long-nosed armadillos Dasypus novemcinctus Linnaeus, 1758 from Pantanal wetlands, Aquidauana sub-region, Mato Grosso do Sul State, with the description of Hadrostrongylus speciosum n. gen. et n. sp (Molineidae : Anoplostrongylinae)

This study evaluated the gastrointestinal helminth fauna of long-nosed armadillos, Dasypus novemcinctus, from the Pantanal wetlands, Aquidauana sub-region, Aquidauana County, Mato Grosso do Sul State, Brazil. Thirteen species of nematodes, comprising seven genera and four families, were recovered from their gastrointestinal tracts. The following descriptors of infection were determined: prevalence, variation of intensity, average intensity and abundance. Hadrostrongylus speciosum n. gen. et n. sp. is first described here. (c) 2006 Published by Elsevier B.V.

Helminthological screening of free-ranging grey brocket deer Mazama gouazoubira Fischer, 1817 (Cervidae: Odocoileini) from Brazilian Pantanal wetlands, with considerations on Pygarginema verrucosa (Molin, 1860) Kadenatzii, 1948 (Spirocercidae: Ascaropsinae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação da dor pós-operatória: análise em pacientes submetidos a cirurgias abdominais em um hospital universitário de Natal/RN

It is an exploratory and descriptive study made by a quantitative approach, developed among February and May 2010, aiming to assess the pain of patients underwent abdominal surgeries in a University Hospital, in Natal/RN; to identify the local and intensity of the pain based on Numerical Estimative Scale; to analyze the pain related to the sensorial-discriminative, motivational-affective and cognitive-assessment dimensions, using the McGill Questionnaire pain; to establish a relation between the pain process and age, gender, religion, and king of surgery; to identify the medicines efficiency used to control postoperative pain. The sample was composed by 253 patients underwent abdominal surgeries. The results showed a total of 63.63% females between 38 and 47 years of age (21.34%); illiterates (21.73%); married (64.03%), living in Natal and surroundings (67.97%) and Catholics (74.30%). In their first assessment, 84.19% showed postoperative pain; the pain was considered light in 18.97% of them, moderate in 21.74% and severe in 43.48%. The mean number of descriptors chosen through the McGill Questionnaire Pain was 10.78 (DP= 6.09) and pain rating 23.65 (DP= 15.93). The descriptors selected with higher frequency were: sickening pain (69.01%), tired (65.25%), thin (62.44%), bored (58.69%), ardor (46.48%), pointed (38.50%) and colic (35.21%). In their second assessment, 57.71% of patients didn t relate any postoperative pain and 42.29% were still complaining about the pain. After taking analgesic medication, just 41.90% of patients who had complete pain relief. The Pharmacological groups most used were: simple analgesic (37.86%), weak opioids (32.98%), AINES (19.85%) and strong opioid (9.31%). It was not found a significant postoperative pain variation related to the sexes, religion and kind of surgery. It was concluded there were a high level in the number of patients with postoperative pain, mainly in a severe scale. Less than half of patients had the pain relief. Then, it was observed there was not coherence between the pain intensity and the analgesic it was used. To solve or relieve this kind of problems is necessary a permanent education to the health professionals who works in this area

Caracterização morfológica de plantas de rambutan

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Compositional analysis for an unbiased measure of soil aggregation

Soil aggregation is an index of soil structure measured by mean weight diameter (MWD) or scaling factors often interpreted as fragmentation fractal dimensions (D-f). However, the MWD provides a biased estimate of soil aggregation due to spurious correlations among aggregate-size fractions and scale-dependency. The scale-invariant D-f is based on weak assumptions to allow particle counts and sensitive to the selection of the fractal domain, and may frequently exceed a value of 3, implying that D-f is a biased estimate of aggregation. Aggregation indices based on mass may be computed without bias using compositional analysis techniques. Our objective was to elaborate compositional indices of soil aggregation and to compare them to MWD and D-f using a published dataset describing the effect of 7 cropping systems on aggregation. Six aggregate-size fractions were arranged into a sequence of D-1 balances of building blocks that portray the process of soil aggregation. Isometric log-ratios (ilrs) are scale-invariant and orthogonal log contrasts or balances that possess the Euclidean geometry necessary to compute a distance between any two aggregation states, known as the Aitchison distance (A(x,y)). Close correlations (r>0.98) were observed between MWD, D-f, and the ilr when contrasting large and small aggregate sizes. Several unbiased embedded ilrs can characterize the heterogeneous nature of soil aggregates and be related to soil properties or functions. Soil bulk density and penetrater resistance were closely related to A(x,y) with reference to bare fallow. The A(x,y) is easy to implement as unbiased index of soil aggregation using standard sieving methods and may allow comparisons between studies. (C) 2012 Elsevier B.V. All rights reserved.

Trends of the genetic connectedness measures among Nelore beef cattle herds

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classificação de distúrbios na rede elétrica usando redes neurais e wavelets

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

Reconhecimento de voz através de unidades menores do que a palavra, utilizando Wavelet Packet e SVM, em uma nova estrutura hierárquica de decisão

The automatic speech recognition by machine has been the target of researchers in the past five decades. In this period have been numerous advances, such as in the field of recognition of isolated words (commands), which has very high rates of recognition, currently. However, we are still far from developing a system that could have a performance similar to the human being (automatic continuous speech recognition). One of the great challenges of searches for continuous speech recognition is the large amount of pattern. The modern languages such as English, French, Spanish and Portuguese have approximately 500,000 words or patterns to be identified. The purpose of this study is to use smaller units than the word such as phonemes, syllables and difones units as the basis for the speech recognition, aiming to recognize any words without necessarily using them. The main goal is to reduce the restriction imposed by the excessive amount of patterns. In order to validate this proposal, the system was tested in the isolated word recognition in dependent-case. The phonemes characteristics of the Brazil s Portuguese language were used to developed the hierarchy decision system. These decisions are made through the use of neural networks SVM (Support Vector Machines). The main speech features used were obtained from the Wavelet Packet Transform. The descriptors MFCC (Mel-Frequency Cepstral Coefficient) are also used in this work. It was concluded that the method proposed in this work, showed good results in the steps of recognition of vowels, consonants (syllables) and words when compared with other existing methods in literature

Análise e otimização de superfícies seletivas de Frequência utilizando redes neurais artificiais e algoritmos de otimização natural

The bidimensional periodic structures called frequency selective surfaces have been well investigated because of their filtering properties. Similar to the filters that work at the traditional radiofrequency band, such structures can behave as band-stop or pass-band filters, depending on the elements of the array (patch or aperture, respectively) and can be used for a variety of applications, such as: radomes, dichroic reflectors, waveguide filters, artificial magnetic conductors, microwave absorbers etc. To provide high-performance filtering properties at microwave bands, electromagnetic engineers have investigated various types of periodic structures: reconfigurable frequency selective screens, multilayered selective filters, as well as periodic arrays printed on anisotropic dielectric substrates and composed by fractal elements. In general, there is no closed form solution directly from a given desired frequency response to a corresponding device; thus, the analysis of its scattering characteristics requires the application of rigorous full-wave techniques. Besides that, due to the computational complexity of using a full-wave simulator to evaluate the frequency selective surface scattering variables, many electromagnetic engineers still use trial-and-error process until to achieve a given design criterion. As this procedure is very laborious and human dependent, optimization techniques are required to design practical periodic structures with desired filter specifications. Some authors have been employed neural networks and natural optimization algorithms, such as the genetic algorithms and the particle swarm optimization for the frequency selective surface design and optimization. This work has as objective the accomplishment of a rigorous study about the electromagnetic behavior of the periodic structures, enabling the design of efficient devices applied to microwave band. For this, artificial neural networks are used together with natural optimization techniques, allowing the accurate and efficient investigation of various types of frequency selective surfaces, in a simple and fast manner, becoming a powerful tool for the design and optimization of such structures

Seleção seqüencial de descritores por análise da semântica para recuperação de imagens baseada no conteúdo

With the rapid growth of databases of various types (text, multimedia, etc..), There exist a need to propose methods for ordering, access and retrieve data in a simple and fast way. The images databases, in addition to these needs, require a representation of the images so that the semantic content characteristics are considered. Accordingly, several proposals such as the textual annotations based retrieval has been made. In the annotations approach, the recovery is based on the comparison between the textual description that a user can make of images and descriptions of the images stored in database. Among its drawbacks, it is noted that the textual description is very dependent on the observer, in addition to the computational effort required to describe all the images in database. Another approach is the content based image retrieval - CBIR, where each image is represented by low-level features such as: color, shape, texture, etc. In this sense, the results in the area of CBIR has been very promising. However, the representation of the images semantic by low-level features is an open problem. New algorithms for the extraction of features as well as new methods of indexing have been proposed in the literature. However, these algorithms become increasingly complex. So, doing an analysis, it is natural to ask whether there is a relationship between semantics and low-level features extracted in an image? and if there is a relationship, which descriptors better represent the semantic? which leads us to a new question: how to use descriptors to represent the content of the images?. The work presented in this thesis, proposes a method to analyze the relationship between low-level descriptors and semantics in an attempt to answer the questions before. Still, it was observed that there are three possibilities of indexing images: Using composed characteristic vectors, using parallel and independent index structures (for each descriptor or set of them) and using characteristic vectors sorted in sequential order. Thus, the first two forms have been widely studied and applied in literature, but there were no records of the third way has even been explored. So this thesis also proposes to index using a sequential structure of descriptors and also the order of these descriptors should be based on the relationship that exists between each descriptor and semantics of the users. Finally, the proposed index in this thesis revealed better than the traditional approachs and yet, was showed experimentally that the order in this sequence is important and there is a direct relationship between this order and the relationship of low-level descriptors with the semantics of the users

Estudo do acoplamento entre superfícies seletivas de frequência assimétricas em estruturas de multicamadas

This work presents the development of new microwaves structures, filters and high gain antenna, through the cascading of frequency selective surfaces, which uses fractals Dürer and Minkowski patches as elements, addition of an element obtained from the combination of the other two simple the cross dipole and the square spiral. Frequency selective surfaces (FSS) includes a large area of Telecommunications and have been widely used due to its low cost, low weight and ability to integrate with others microwaves circuits. They re especially important in several applications, such as airplane, antennas systems, radomes, rockets, missiles, etc. FSS applications in high frequency ranges have been investigated, as well as applications of cascading structures or multi-layer, and active FSS. In this work, we present results for simulated and measured transmission characteristics of cascaded structures (multilayer), aiming to investigate the behavior of the operation in terms of bandwidth, one of the major problems presented by frequency selective surfaces. Comparisons are made with simulated results, obtained using commercial software such as Ansoft DesignerTM v3 and measured results in the laboratory. Finally, some suggestions are presented for future works on this subject

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Novas configurações de monopólios planares quase-fractais para sistemas de comunicações móveis

The characteristic properties of the fractal geometry have shown to be very useful for the construction of filters, frequency selective surfaces, synchronized circuits and antennas, enabling optimized solutions in many different commercial uses at microwaves frequency band. The fractal geometry is included in the technology of the microwave communication systems due to some interesting properties to the fabrication of compact devices, with higher performance in terms of bandwidth, as well as multiband behavior. This work describes the design, fabrication and measurement procedures for the Koch quasi-fractal monopoles, with 1 and 2 iteration levels, in order to investigate the bandwidth behavior of planar antennas, from the use of quasi-fractal elements printed on their rectangular patches. The electromagnetic effect produced by the variation of the fractal iterations and the miniaturization of the structures is analyzed. Moreover, a parametric study is performed to verify the bandwidth behavior, not only at the return loss but also in terms of SWR. Experimental results were obtained through the accomplishment of measurements with the aid of a vetorial network analyzer and compared to simulations performed using the Ansoft HFSS software. Finally, some proposals for future works are presented